SNS: Smart Node Selection for Scalable Traffic Engineering in Segment Routing Networks

• Published in: IEEE eTransactions on network and service management 2024-07, p.1-1
• Main Authors: Wang, Linghao, Lu, Lu, Wang, Miao, Li, Zhiqiang, Yang, Hongwei, Zhu, Shuyong, Zhang, Yujun
• Format: Article
• Language: English
• Subjects: Computational modeling; Costs; Network topology; Reinforcement Learning; Routing; Routing Optimization; Scalability; Segment Routing; Task analysis; Topology; Traffic Engineering
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2024.3424928

Segment routing (SR) is an emerging architecture that can benefit traffic engineering (TE). Nowadays, TE in SR networks (SR-TE) is often solved as an optimization problem to optimize network performance such as link utilization. As network size grows rapidly, implementing SR-TE suffers from scalability issues, including long computation time, high control overhead and expensive deployment cost. In this paper, we propose Smart Node Selection (SNS), a scalable SR-TE method with learning-based node selection (NS). NS is a recently proposed technique for reducing computation time of SR-TE. It first selects a subset of nodes as candidate intermediate nodes to route traffic, then builds linear programming (LP) models that can be solved efficiently. However, existing NS methods use simple heuristics and consider only network topology, which may lead to unsatisfying network performance. To address this problem, we for the first time formulates NS as a reinforcement learning task, which learns a selection policy to achieve better trade-offs between TE performance and computation time, considering both topology and traffic. Besides, we extend NS with additional selection policies and a customized training algorithm, making it a unified framework for scalable SR-TE, which reduces not only computation time, but also control overhead and deployment cost. Performance evaluations on various real-world topologies and traffic matrices show that SNS significantly reduces computation time and control overhead of existing LP models while offering good network performance, and can also be used in partially deployed SR networks to reduce deployment cost.